{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras import applications\n",
    "from keras.models import Sequential\n",
    "from keras.models import Model\n",
    "from keras.layers import Dropout, Flatten, Dense, Activation\n",
    "from keras.callbacks import CSVLogger\n",
    "import tensorflow as tf\n",
    "from scipy.ndimage import imread\n",
    "import numpy as np\n",
    "import random\n",
    "from keras.layers import LSTM\n",
    "from keras.layers import Conv1D, MaxPooling1D, LeakyReLU\n",
    "from keras import backend as K\n",
    "import keras\n",
    "from keras.callbacks import CSVLogger, ModelCheckpoint\n",
    "from keras.backend.tensorflow_backend import set_session\n",
    "from keras import optimizers\n",
    "import h5py\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import os\n",
    "import pandas as pd\n",
    "# import matplotlib\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID'\n",
    "os.environ['CUDA_VISIBLE_DEVICES'] = '0'\n",
    "os.environ['TF_CPP_MIN_LOG_LEVEL']='2'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "config = tf.ConfigProto()\n",
    "config.gpu_options.allow_growth = True\n",
    "set_session(tf.Session(config=config))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with h5py.File(''.join(['bitcoin2015to2017_close.h5']), 'r') as hf:\n",
    "    datas = hf['inputs'].value\n",
    "    labels = hf['outputs'].value\n",
    "    input_times = hf['input_times'].value\n",
    "    output_times = hf['output_times'].value\n",
    "    original_inputs = hf['original_inputs'].value\n",
    "    original_outputs = hf['original_outputs'].value\n",
    "    original_datas = hf['original_datas'].value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "scaler=MinMaxScaler()\n",
    "#split training validation\n",
    "training_size = int(0.8* datas.shape[0])\n",
    "training_datas = datas[:training_size,:,:]\n",
    "training_labels = labels[:training_size,:,:]\n",
    "validation_datas = datas[training_size:,:,:]\n",
    "validation_labels = labels[training_size:,:,:]\n",
    "validation_original_outputs = original_outputs[training_size:,:,:]\n",
    "validation_original_inputs = original_inputs[training_size:,:,:]\n",
    "validation_input_times = input_times[training_size:,:,:]\n",
    "validation_output_times = output_times[training_size:,:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(221, 272, 1)"
      ]
     },
     "execution_count": 315,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ground_true = np.append(validation_original_inputs,validation_original_outputs, axis=1)\n",
    "ground_true.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(221, 272, 1)"
      ]
     },
     "execution_count": 316,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ground_true_times = np.append(validation_input_times,validation_output_times, axis=1)\n",
    "ground_true_times.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "step_size = datas.shape[1]\n",
    "batch_size= 8\n",
    "nb_features = datas.shape[2]\n",
    "epochs = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "metadata": {},
   "outputs": [],
   "source": [
    "#build model\n",
    "\n",
    "model = Sequential()\n",
    "\n",
    "# 2 layers\n",
    "model.add(Conv1D(activation='relu', input_shape=(step_size, nb_features), strides=3, filters=8, kernel_size=20))\n",
    "# model.add(LeakyReLU())\n",
    "model.add(Dropout(0.25))\n",
    "model.add(Conv1D( strides=4, filters=nb_features, kernel_size=16))\n",
    "\n",
    "'''\n",
    "# 3 layers\n",
    "model.add(Conv1D(activation='relu', input_shape=(step_size, nb_features), strides=3, filters=8, kernel_size=8))\n",
    "# model.add(LeakyReLU())\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Conv1D(activation='relu', strides=2, filters=8, kernel_size=8))\n",
    "# model.add(LeakyReLU())\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Conv1D( strides=2, filters=nb_features, kernel_size=8))\n",
    "model.summary()\n",
    "\n",
    "# 4 layers\n",
    "model.add(Conv1D(activation='relu', input_shape=(step_size, nb_features), strides=2, filters=8, kernel_size=2))\n",
    "# model.add(LeakyReLU())\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Conv1D(activation='relu', strides=2, filters=8, kernel_size=2))\n",
    "# model.add(LeakyReLU())\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Conv1D(activation='relu', strides=2, filters=8, kernel_size=2))\n",
    "# model.add(LeakyReLU())\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Conv1D( strides=2, filters=nb_features, kernel_size=2))\n",
    "'''\n",
    "model.load_weights('weights/bitcoin2015to2017_close_CNN_2_relu-44-0.00030.hdf5')\n",
    "model.compile(loss='mse', optimizer='adam')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 221, 16)\n",
      "(3536,)\n"
     ]
    }
   ],
   "source": [
    "predicted = model.predict(validation_datas)\n",
    "predicted_inverted = []\n",
    "\n",
    "# In[7]:\n",
    "for i in range(original_datas.shape[1]):\n",
    "    scaler.fit(original_datas[:,i].reshape(-1,1))\n",
    "    predicted_inverted.append(scaler.inverse_transform(predicted[:,:,i]))\n",
    "print np.array(predicted_inverted).shape\n",
    "#get only the close data\n",
    "ground_true = ground_true[:,:,0].reshape(-1)\n",
    "ground_true_times = ground_true_times.reshape(-1)\n",
    "ground_true_times = pd.to_datetime(ground_true_times, unit='s')\n",
    "# since we are appending in the first dimension\n",
    "predicted_inverted = np.array(predicted_inverted)[0,:,:].reshape(-1)\n",
    "print np.array(predicted_inverted).shape\n",
    "validation_output_times = pd.to_datetime(validation_output_times.reshape(-1), unit='s')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>times</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-06-03 16:30:00</td>\n",
       "      <td>2444.999900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-06-03 16:35:00</td>\n",
       "      <td>2440.999999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-06-03 16:40:00</td>\n",
       "      <td>2450.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-06-03 16:45:00</td>\n",
       "      <td>2455.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-06-03 16:50:00</td>\n",
       "      <td>2456.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                times        value\n",
       "0 2017-06-03 16:30:00  2444.999900\n",
       "1 2017-06-03 16:35:00  2440.999999\n",
       "2 2017-06-03 16:40:00  2450.000000\n",
       "3 2017-06-03 16:45:00  2455.000000\n",
       "4 2017-06-03 16:50:00  2456.000000"
      ]
     },
     "execution_count": 320,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ground_true_df = pd.DataFrame()\n",
    "ground_true_df['times'] = ground_true_times\n",
    "ground_true_df['value'] = ground_true\n",
    "ground_true_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60112, 2)"
      ]
     },
     "execution_count": 321,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ground_true_df = pd.DataFrame()\n",
    "ground_true_df['times'] = ground_true_times\n",
    "ground_true_df['value'] = ground_true\n",
    "ground_true_df.set_index('times').reset_index()\n",
    "ground_true_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3536, 2)"
      ]
     },
     "execution_count": 322,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction_df = pd.DataFrame()\n",
    "prediction_df['times'] = validation_output_times\n",
    "prediction_df['value'] = predicted_inverted\n",
    "prediction_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>times</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-06-04 13:50:00</td>\n",
       "      <td>1538.269165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-06-04 13:55:00</td>\n",
       "      <td>1528.504150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-06-04 14:00:00</td>\n",
       "      <td>1530.672852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-06-04 14:05:00</td>\n",
       "      <td>1536.479858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-06-04 14:10:00</td>\n",
       "      <td>1542.343140</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                times        value\n",
       "0 2017-06-04 13:50:00  1538.269165\n",
       "1 2017-06-04 13:55:00  1528.504150\n",
       "2 2017-06-04 14:00:00  1530.672852\n",
       "3 2017-06-04 14:05:00  1536.479858\n",
       "4 2017-06-04 14:10:00  1542.343140"
      ]
     },
     "execution_count": 323,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 2016/5\n",
    "prediction_df = prediction_df.loc[(prediction_df[\"times\"].dt.year == 2017 )&(prediction_df[\"times\"].dt.month > 7 ),: ]\n",
    "ground_true_df = ground_true_df.loc[(ground_true_df[\"times\"].dt.year == 2017 )&(ground_true_df[\"times\"].dt.month > 7 ),:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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1ca9fsWVvyHFNpUujB/VObnI58qMLjtSAoPAsnDvZb2qWeTxd82nqwbvQ9SQE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MaqhibyM5Ztw5MGQbRQ+/XsdQ1lVG43e8w2KKXEf3aqcobBz3+E7dOuCbuG8Cu3/hcDszv5lMW2\nibPhVG09NjzmQDevpu/eCq7ohbe/VK9Ffz2ziIhijt7saydOGK8vJZCa6jubWmUlUFPju/6YMdYe\nr8Oh34Dbn/R039nkbDZ1zkREQRZen7SJTKiqrkVTJZvurI3Pp+Q1e7uL727MDthbcRKdApydqKvH\nTWFigt3n8d5dkjEwu5NPMAkAUpISMG/8QL/bd0/hvWHXYU0wKTMtSRNMopb74omrMPOX52jGctM7\nhOholPxBqkfVN08Ob1Z/LX9mj72g4fs3Pt2N+nqJL/dUYPW2A/i89EiLtm3W+l2HDcsMvYNJALBq\n2wHU1Uv0enQJsh9dqnms75T3UVsXml4X7369D4++8y2yJi9B9qNLMG1JCTbsOow7F2zE2VM/QL1r\nZr5Zq3cia/ISnKqtR9t4W5PBJADo07U9PpviwO7nRyIpgX8XIiIC0Dj7mjuw0tzZ1zyDSe5lvWBS\noOx2FQTyHisuBlasAAoK1LJ73HNZb1v79qlAmOcXg0lEFCK866SItHrbAeQ8uRwrtx5oGFv+wFCf\n9d6//zKsfeRKdGnfplnb3fXcSJzTI6WhT9HIs09r0XG6m2+neQWlunVogyX3Xdbk85c9cBnud/TF\ntmdH4O2CS7DzuZGY6soeqayuRW1dPX7tMcvXry48HX9txUbb1Mio8XqwTL0uF59OHoaOSS0/Du/r\no9djS/GLov/gjnmf45evrsOOA82feaylxuhMP+8Z8PI2ft7n6P3YUtQbxJZLD1ehorI6qIGlI5XV\nuP/Nr7H4sz0A1Gf919bu1lyrvR5biqzJSzTlqqlJzQ9ed23fNmz6XRFRK3I6gawslYGSldVYcpWR\noS3Fysiwfh+RZs6c0O7f4dAfnzjRNwhUW9vYu6moSC27x4uKgAUL9LdlNE5EFCJsyk0RaczrG/DJ\njkMNyy/9+hzceN7pDZkNZpoLP/3vErzx6W4AjY1wT5yqxbxPdqPgit6WZfuUHa7EvE9LMf8/pS0q\nxZmx7DsUrd6p+1g0Ny0OB4s/24N3v94H510XW94/KZSklD7ZPXrWPHwleqYlNWub1bX12HOkEn26\nmuvv45md9Le7BqFDYjz6Z3TELXPWYf0ulS31+KizsHb7IXz8vX5J6D/vHYJ/f1OOuZ/s1ox3Sk7A\nxil5Lc7oakrZ4UpcPnO1qeeMPPs0vHLr+a1+bEQUQZxO4PbbtbOK2WxAhw6+WTWAyoLZt8/8Prwb\nVgsBLFoUebODBTPIXlysP5uaXsldUVFg+3A6OWMbEYUMm3JTVPMMJgFATZ02MGrmL/d6/WLatYnD\nbx19LS0dy0xLxtTrclE6fVSL+rqsNuirdM/Q8JvyPdrcOrAn3pw4OKqCSYC6Xkqnj/KbCQQAQ2f6\n9gEzct/ir5D34ho89e7mZj+nulabRXRJn87on9ERADDp8t4N43demo0ZNw/Q3UbX9m1wbo8U5J3V\nzeexI5XVeGG5bwN7q9V6pEvNvHkAvntmRMPyxKG98NBV/dAxsTGz7KOHLkdR/gUMJhFFO7OZQHfd\n5TtFfX29fjAJUH11/O1D77E77vBtWC2lGid9aWkqsFNaqn4fpaX+s40CZbQPIqIwwuYLFFGMMuqG\nndkVAJDTvQNK9h8P5iEF3bzxF2HQc74NHP8w4swQHA1Fk6t0gjBmbd1/HJ/tPoJlW34EACxYV4YB\np6fgJo/m2Ub6Pf5+w/fe2XYXZXUCABRc0RtCCHTr0BbTbuyPQdmdkPfiGgDAF4/nIa2dKt8bbDDL\nomcgpynVtfVYuK4U44dkw24TqKuXsAnjgPVv/vYlzs7oiKH9ugAAXh1zPkb0797w+JA+aXhspGqi\nzUb5RDHG6QTGjWvsdVNWppYB4NNP9bNafv7Z/H7Gj2/s/1NWppbdPBtKl5X5bzBdW2t+3+EsLQ04\nfNiabb38sjXbISKKAgwoUUQxKstx9yj6571DUOv917wo061DW2R3TsbuQ5UAgA5t47Bp6tUhPiqK\nBt4ZMr/L64eXVnzf7OdX19bjmpfX+oy7X6t66uslJr+zCVNGNmYK6gV9ktvE+QSZ8gepmfZ2TLsG\nOw9WNgST3CYMycYbn+7GRVmpKLyiD8bP/9xw5mg97gDXs0u2asbHXNwTz95wtmbsPzsP4b1N+/He\npv14/n2VBeWZichyVKIYd889vo2T6+q0QSb32KxZwMcfB7Yf72bSNTVq36dOmd+Wu8m1p5wcYMuW\nwI7NSnl52tnRjPoXub38sjbYBgDx8f6bb6ekaLPBIrUUkIioFbHkjSLGVj+ZR+6MgYQ4W0CzHq3+\n/RX4928uDfjYgu1yVwYEAJzbMzWER0LR5uGrz2j4/v68vnDeNUjzeL1B9+s9h6s0GUaezuuZglc+\n2o5z/vgBFq0r1Tw25Z/f4q2Ne3HO0x80jD0y4gyYEWe34YzTfHs1PXltDnY/PxJ/n3QJrnRlMc5c\nvg0AcPznGmRNXoILnvlQd5sHjhtnBhSvV822l23+Eadq1Y3gba/5NhPXOyYiilGVBoF1o9m5Skqs\n3XcgGUd6M6SVlAC5uSrYFBengixxcWo5WLyDSYDvsrf8fGDePCAzUx1zZqZaTk7WXz85Gaio0DbS\nrq9nMImIyAszlChiTPnHt5rlF246G1/tOWpJqVdWZ4MPFGGq3iPNoiifs7qRde69sg/O6NYeA3up\nErMhfTqjdPqohmbZ72/+EaMGdPd53swPthlus7q2Hn/6QGU6PfHuFowakI5OyQmor5dY/NkPmnUz\nUhIbMo+soFee9vH3B7HzwAkAwOHKahw7WaPJiiopP46Rf/bNtPL02ppdmLZUZS5Nu7E/BpzeEZv2\nHsNlfTtj7fZDmHHTAJye2rwG5kQUZfSaKUeTkhJtwMudVQW0rGdQczUVPDKSn68fEPLOErPbgdmz\nA9sHEVGM4SxvFPb2HK7SNAOed8dFyEnvgG4d2obwqEJr2eb9mFT8JZIS7Ch5ekTTTyBqIXdAqUv7\nNvh8Sp7h455WPHg58l7UL9vYMe0azP9PqU852UcPXY5eXdpZcMS+9I4RAJ4YnYM7L83WXe+V287D\nzOXbsGD8QNTWS8PzcWNpG1GMczr99yaKZkL4NhFvrf2Y5e9+h7OpERH5aO4sb8xQorD3hNcsUe7S\nlVhmt6lq1cG99BsPE1ntF+dl4J2v9uHgT83rw3FN/9MQbzf+0H/J9I9wwGNbV5zRBZv2Hmu1YJI/\nz7xXgi7t2+BkdS3+8HZjJuQFmakYPSAdowekN2s7WWnMSCKKee5G27EoQv9IbZi5RERETWJAicLe\nx98fbPj+oav6hfBIwkdKkirP6RrDWVoUXA9dfQbe+WofAKCmrl7TcNrbfcP64MHhZ2DXwROG63gG\nk+bdcVFQAsVrHr5Sk+3o6b7FX/mMvV1wic+YZ/nf8geGoqauHrsPVeLac5oXdCKiKGfUEylQDodv\n8+l+/fT7G1kpPR0oL2/dfRARUcRjU24KS1JKZE1eoik9ue6cdNx7ZZ8QHlX4uCirE16+5Vw8Mfqs\nUB8KxYi6usa/PDvXl/ldd3juaQCA1KQEzfg/Ci/BLRf18Fl/cO/gZNr19MogWvPwlQFtp3T6KJRO\nH4UzTmuP/hkdGUwiotaRnAysWKFtDL1ihepTVFCgev0A6t+CAsAW4Md67xnSHA5g377AtuV0AllZ\n6liystRyqBUUhPoIiIiiFgNKFHbq6iWWbf5RMzZ+SBb+fOt5PtOax7Lrz80IaEY7okCcnprY8P3U\nf/uffah/RkcAQGqyNqB0Xs9UTL9pAKZem6MZbxtvt+gom/bNU8Ox5L5LUTp9FHqmJWH5A0M1j7vL\n1j57rIkpqIkotgUjcOKvMXRRkZq5TUr1b1ERcM89ge1HL2gF+AaammPsWKCsTG2nrEwtN/WzMfpZ\nFhaqMSHUV/v2TW9LL9AWjEbhREQxik25qUUqT9Vi9sc7UbxhDzZOyWtxwKe+XqLXY0t9xr9/9hok\nxDH+SRRKnhmDL99yLhLj7cjN6IiMlETNY56NqaWU+MPbm/D46Bx0aBvvs61waGL99hd78dDfvwEA\n7H5+pO7McEREDZxOFSjx/AwtBLBokfmG3AkJQFwcUFWl3dakSYEFQvLy9GdB8y6dc2sq4OK9PaPt\n+NOmDfDzz/qPOZ3A+PFATU3jWHw8MHRoYLO5Reh9DRFRuGluU24GlMiUHQdOILtzMuyuwJH3rElv\n3TMYv5q9DrcN6olpN/T3e2NWVV2LXQcrMfovnyAtOQGHK6txxRldsHpbY8+kHdOuQZyfXi1EFDx6\ns6R1bd8Gn03Jw9i5G7B2+yEAzQsS/XXVDrRvG4fbB2dZfZhERK0rPl5lBXmLi9Mfd7PZtLOg2WzA\nwoXqeytnGTOataywEJgzR/V5stuBiRMDC1pZOcta587A4cPmt2d2P0REZAoDSmS5ssOVuHzmagDA\npqnDMWDqB00+Z1B2Jzx1bS5y0jtoxqtr69Hv8fcNnzfjpgH4xfkZDCYRhZHPdh/Br2av8xkvefpq\n5Dy5HADw9ZNXIcWrdxIRUcTKzQVKPMp8c3K0y2YUF0fH9PSBBpT0Al1mM7r8sdv9B/SIiKjZmhtQ\n4t06NdvzS79r+L45wSQA2LD7CEb+eS0A4IcjVej92FLM/3Q3hr/0sd/n/eqiHgwmEYWZgdmddMdL\nyo83fM9gEhFFDe9gEhB4MAlQwaPSUpWlVFoamcGkQDmdwIQJ2v5KEyZYu48FC6zdHhERNYkdfWOY\nlBLf7juGnO4dmhW8qaiq1h0vnT4Kew5XNUzH3atLMnYdrNSss37XYdwyZz0AbUPfjJRELJgwEClJ\n8ejcrk2gp0JEIXTzq75ZS0REEcPpVA2tK12fXWw2tRxI8EgI/bKrWO/NNmkSUO31OdJ72YxoyfYi\nIopwDCjFsFXbDmDCfFU26DizK569sT+6d1QzOZ31xDKcrKnDn289D9e5psTesPsIAGDmzQPw8P9t\nwpqHr2yYhrtnWhI+nTwMJ6vr0KdrO/x47GfsOVKFD7b8iNc/2d0QTPI05uKeePaGs4NxqkRkkQsz\nU7GxrCLUh0FEVtMr7dqyBcjIAMrLG8fT0wOfUj7U9HoIDRniW3ZVXw/MmhXYPiZN0n/upEmBbS9a\nnDhh7fby8xlAIiIKA6wpimGb9zWWqaz87gAGP/8RAFWadrKmDgBw3+KvkDV5iaYZ7y8v7NEw5ban\njJRE9OnaDgBwWse2GJjdCVmdkzXrdO/YFqMHdMcrt53HYBJRBGImIVEUMirtstu1wSRALWdkqO+N\npnsPR4WFKtBTpz7foK5OLVvZwwdQTa6jfer6goLQ7j85uel1iIgoKBhQikHvfr0Pt722Hi9++L3P\nY1mTl+CyGasMn/vUtTmm9vWrC3toltc96sArt52P0QPSTW2HiMJD4ZW90aU9g0pEUcWotMtzRjJP\n5eUqeDRmjLYnzpgx1geVMjJUuZj7K9Bg1pw51h6XnkSV5Y2iItUcWkr1bzQFkwDjoJnVHA7fMZsN\nmD3b+n0REVFAOMtbjBk6YxX2HKlq1rql00dpMpPuc/TFg1f1M71PKSVufW09XrhpADLT+Fclomhw\n65z1qKuX2FtRhfJjPwMAFk4YiKH9uoT4yIjCXF4esHJl47LDAaxYEbrjAazv75OSAhw9ql2uCKBU\n1rvczmj7bsXFxmVQVp9jYiJw8qR2uap5n6+iVtu2wKlT1m3PaGY4lroREbU6zvJGuoyCSU+M1mYe\nPT7qLADAt1OHo2/XdvjumREBBZMAQAiBNycOZjCJKIr87e5BeGvS4IZgEgDEc2ZGijV5edrsmbw8\nNW6UPZObqw0mAWo5L8/4OZFUVubmHew5ehRITTW/Hb1gkt723dzlawkJ2t9LQivMPllVpQIe7q9Y\nDyYBwNy51m8zlmfGIyKKAGzKHUMqT9X6jJVOH4VNe4/i7IyOuH1wJvpOeR/rH3XgtI5tAQDt28bj\nwwcvD/ahElGYEzp/7U+IY0CJYoh3phGglr37EblLwT791Li0bOVK7bY8n+PZ4Nk9Dvi/sdZrPl1U\nBCQlabNqANVkOxjcQSWjzCW95t+B0MtEqqkJbFtG4uOt3V60yM/3fc0CKqDXkhndiIgobPHTfwzx\nnBmrv3AAABgwSURBVJnpzYkXY+dzIwEAA05PgRAC8XYbSqePaggmERGZ0YYBJYol3sEkN6OgUSCz\nhhk9xx1U0usvZNR82m73DSYBxllArcEoc0mvtC2Yx5WSoj9eUOAbPIqPZ3DEn6IiVXqYmalek5mZ\nwBtvmN9OZqb1x0ZERJbjp/8YYnP90W5W/vm4uFca7DaL+wkQUczp0LYx0ZUlb0RBlJSkH4QxCkIZ\nNdgOtaNHgxs80lNRYTwzW3W1trSNwaSmmS1TS0ryXZ42rbWOjoiILMRP/zHkZLX6a2WPTklNrElE\n1DxfPTm84XuWvBEFkV62EQUu2mdmC2dz5mgzmubMYa8kIqIIwU//MaSyWvVQSkywh/hIiCha2G0C\nqUmqJMRu9SxKROHCqPl2tMvMBHK0k3YgJ8d4ivhg9WOi6MLG20REEYsBpTCwdvtBzFz+Xavv50il\nakqZkshmkkRkneQ2quwtzs6AEkUho+bb0cao7GjLFm3J15YtKntHr0Rs377gHGtOjnHwymbyo63D\n0fLjoaYZ9URiryQioojGgFIYGDv3M/x11U5884PBNLgB2nO4CjsPnsC+oyot/sOSHwEAnZJbYfpc\nIopZc8ddhAlDstGdDf0pGlkVPEpMtGY7rcHhMF92ZFQiZnWARi9DassWFbzybv5cXKyakOs10pbS\n99gcDmDFCmuPl/RNm8ZeSUREUUhIKUN9DAG58MIL5caNG0N9GC2yt6IKl76wqmH56txumD32Qku2\nvePACeS9+LHuY6XTR1myDyIioqhntpRTCKBtW22Po8REoKpK3UDr9T6y2fSbZhcUAIsX+86OZrR+\nU4qLgQkTtI2lWyOo4p3V5XCo6eR//tn8tiL0cyrpcDqBKVOAPXuAnj1VMInlbUREYUkI8YWUssng\nBDOUQujLPdoPiMdO1qAlAT7nhjL85m9fYvO+Y4bBpPuG9Ql4+0RERDHF6TT/nEmTVPDIs0ysqko9\nVlXlm6mUmKiyaoxmGauo8M3EWbgwsPPJzwdOndIeW2tk6KxY4buP11/XX9duN85qMurVRJGJvZKI\niKIOM5RC7PxnPsQfr8vFbxd/BQAYPaA7fjhShXN7pOCP1/fHyeo6xNsF7DYBYfBXUiklzn36Qxw7\nWePz2KtjzkdqUgIefOsbFOWfj3N6pLTq+RCFncJCVbpRV6duXCZO5Ow9RLHMTJZE587A4cPG2yoo\nCN37i9nMqYQEFUwKJacTuP/+xp9pWhrw8svq58/3aiIiorDR3AwlBpTCRNbkJU2uk3dWV7w+7iLU\n10tU19XjwPFTGDpzleH6T12bg/FDsq08TKLIUlgIzJrlO+7+yz8RRS+9sqvx44ExY3zXLS4G7r7b\nt0xNrzzNUyg/Q+k1CwdUj6GSEt/x4mJmhBAREVGzMKAUYZoTUGrK2Isz8cwN/VFdW4/tB35CbnpH\nC46MKALk5mpvoNxNW/39BT9C3/uIqBm83xNag/t9JpT0gmYrVrBXDREREbUIeyhFuG4d2gAAMlKa\nnhVmxYNDsfv5kXjmhv4AgIQ4G4NJFDv0bhxLSlR5hz95eSrg5P7Ky2u9YzSzf6cTyMpSTXezsgLr\n4UIUy5zO1g8mAaEPJgH6vYoA9qohIiKioGCGUpjYceAEnvjnZqzbdRgrHhyKPl3b46efa9C+rZr6\ndsay71C0eid2Pz8SPxw5iY5J8eiYGN/EVokiUEICUOPVDyw9XU0RbbZniFkOB7B/v362UyCcTmDs\nWG02VHo6cNZZLFUhsoLdrp3tzGZT7yGBzCZmVoR+fiIiIiJqSqtnKAkhegghVgkhtgohtggh7neN\nTxVC7BNCfO36GunxnEeFEDuEENuEEFd7jI9wje0QQkwO9JgiWZ+u7bDwzoF477eXok/X9gDQEEwC\ngEdGnInS6aMghEDPtCQGkyg66QWTAKC8vPWDSYAK8uhlO2VkqO/NZA85napXi/dNZ3m5fjDJvS89\nej1fiFpDJGXIeQeTALUcjGASEREREbWo5K0WwENSyrMAXAzgXiFEjuuxl6SU57q+lgKA67FbAOQC\nGAGgSAhhF0LYAfwVwDUAcgDc6rGdmBJvt6F/BkvVgqqwEIiLU8GKuDi1HKiMDG0JkzsIEYms/LmY\noRdMCgfl5epnMHEiUFamgkRlZWrZ6VRld56/+9xcYNy4UB81kTmpqSp46fkaHzNGvcbtdu1r3D29\nfSCMglb+ylD13l+9g0nBlBOTH1OIiIiINAIOKEkp90spv3R9/xOArQD83UFfD+BNKeUpKeVuADsA\nDHR97ZBS7pJSVgN407UuUetyzwBWV6eW6+rUsr/gidFNVWqqCjp4Ki9XNz2hCs4Eyt/Pxej8Pcfc\nX+7zNPOccDZnDlBVpR2rqlKBI72sJvfPz0pGQUujG3R/rz29IBhFF6Pfsd61l5EBHD2qv50xY/Qz\ngex2868jd+aed9AqN9c3c2/lShVUysjQf38NhM3kxx6bzTd4FA7NuImIiIjCgCU9lIQQWQDWAOgP\n4EEAdwA4DmAjVBZThRDiFQDrpZTFrufMBfC+axMjpJR3ucbHAhgkpfyNv31GWw8lCgGzM4DplVcE\nKpjT1hcWqmBIXZ06h4kT1b6NxsM9sGNGSorxTXK0MDpHh0O/tK6gAPj4Y/3yOt4ohy+jmQybu34w\n5eQAjz0G3HMPUFmpxmw2tTxrVmiOyU1KICkJOHmycSwxUQWH9foxtUZgmIiIiCjMBW2WNyFEOwBv\nA3hASnkcwCwAvQGcC2A/gP/nXlXn6dLPuN6+JgohNgohNh48eLClh07RxigbQy97o6lyNL1tWVle\n4b6pMsossSp7xCjbKDfXeDxaSAlUVKgAijsrym5Xy9HEKGBm1Kdp1izjQENJSeRl1EUbvfcEo5kM\nc3NV3zHvbKNgzXJmpKREZR25g0mAev8MdTAp0TVralWVdmY0d+ZhXZ12nMEkIiIiIr9alKEkhIgH\n8B6A5VLKF3UezwLwnpSyvxDiUQCQUj7vemw5gKmuVadKKa92jWvWM8IMJdJwB068GWVphIP0dN+y\nDSGA7t31yzlyclTwwPMx9+xnRqIp28jodxnIDGih/rkkJmozJMJdMDPqjOTlaX//DkfjFOnRQm9W\nQCE4m1ggvK8xdxYSERERETUpGLO8CQBzAWz1DCYJIbp7rHYjgM2u7/8F4BYhRBshRDaAvgA+A/A5\ngL5CiGwhRAJU4+5/BXpcFKNefVV/PFyDSYB+0EhK494gJSXGfZoA/w1tI0lxMZCc3Lhss6mAxooV\n6rHMTHV+mZmBBZMAFYzQExenP+55PC1lt6sb25QU7bj3cjgJdWaJdzAJaOyv43Sq14heb6lIa5Q/\nbpxv8IjBJPMcDuMsJCIiIiKyTEtK3oYAGAtgmBDia9fXSAAzhBDfCiE2AbgSwO8AQEq5BcBbAEoA\nLANwr5SyTkpZC+A3AJZDNfZ+y7UuUfPF8k1XebnxDXckys8HTpzQlp24s2Py84HSUlU+U1oaWDAJ\nUMEp76CSwwHMnw/Ex2vH4+OB2bMD2493JpQQwIIF6vuKCu0Nb0VFYPsIlmAFZ/RK7oxeyytXqtIq\n7+u/vFw93ygAa1RqajWjn5lRQ3qWWJmndx1HW+YaERERUZiypCl3KLDkjTRYwtT6jM7RaLygQAVo\n9B6LjwdqanzHA804spLTCUyZAuzZA/TsCUybpo4pkNdYcbH+tozExwO1tYEfe7ClpPgPhBk1fve3\nfqiyoax+7enNTBbOiouBRx7RL6nVC1hbraBA/3fv/oyid/1F6OcXIiIionAXtKbcREHXmpkS7qat\nZkVaCZM/ek2s3SUj3j8fd18Sz0wb91dRkfFzqqutK1+zmlVZUIFsa/58/aym4mLrXk/eGVgtcfSo\ncXaPUUN4f83yjUpXg2HMGOPjAoyzioyamIc6mGT0XpaSYnzt7dunvYbd/dmMMvqMAjo5OcbH5S5h\n9X6PKSrSfx9x8/cYEREREYUEM5QosliZiWSUPWJ2H/4aY8fFRVYZC8tFjJnNHhIisJkBjTKkjB67\n+27jzDFAvzGx0wlMmKACe26BNrBPSwMOHfId93cd5eSEdhYyI8XF0dMUW0ogNVU7C2BTGWVW0puZ\nzmaLrPdDIiIiohjV3AwlBpQoclhdQmL02vd3I+w9M1tTs6w5nY2ZD55sNuNgg5StX8JXXAzMmxf9\ns2ZZyWgGrmHD9AMxwZwZLSnJmhmtAn3d6V1LoS5DDUQ4B4/0jsvo/SVcsv2IiIiIKCKx5I2Cz8pG\nt6mp2tKS1FRrg0nFxeYfKy42Lgkxkp+vX15SV6ff/Nl906hX3hHIja67XEqvvGXFCu22GUzyLz8f\nWLRI+7NctEj93IxKeIIl1DNaBavJtTery0rDNZiUnq4/bvT+wmASEREREQUBM5TIPO8GrQ4HMH68\n8V/Kb79dm43jWfagV5IBaMdaymxjZMB/2VG48ZcJwptLMqN9ezXDXjgyyg40ahjtvX64k1K/TKyp\nLEgiIiIiIoux5I1ah1Wz/dhsQIcO1gWO/M00FsxMkVBo1w6orPQdT0gATp0K/vFQ5HI6gTvu0PaK\niotTfZpCNfuam7//q4xmk9MLfrf2bGXNEaH/7xIRERFRbGDJGzVPXp62tCwvT43b7dpxdzmPVTdj\n9fXWZiFVVYW+7ChUZs9WATpPNhvwxhuhOR6KXPn5aqY5zxKq+fPD/zoqKlJBMCnVv+7j1SvrDFZv\nJ+9r0s1fuS0RERERUQRhQCmW6WUbrVypPztVfX34N9k1uqmMdvn5wMKF2iDAwoUsdaPA5OcDpaXq\nmi8tbXwdZWaG7pgKCqzb1qJF+uNNvb/l5PguS6nfxyk+XmVLsb8REREREUUxBpRiWTiUfljBypvN\nSGUUBCCyyrRpajY5T97L3ryDNO4m8UZNpouLWz/T0N3IOi2tcSwtTQWajN5LCgqALVu02U5btqjH\nKip8m+dXVzfui9clEREREUUpBpQossRqWRtRqOXnqz5Fnhk3c+b4f47erHj5+arJtFH2TjAyDfPz\ngUOHGgNAhw417pvvMUREREREzcKm3LEs3EvYvEXoa5Uoqvl7H+E1S0REREQUcdiUO1IUFqpZlIRQ\n/xYWqnGnE8jKUo1ds7LUcktYvT1vKSn6GQdGJSRxccbNab3LR9xfRERERERERBQWmKEUSoWF+lNx\nOxzAmjVATU3jWHw8MG+e/x4cublASUnjck6O6vORlAScPNny442PbyxF8ZSYqGZZM1JYCLz6auPz\n2rVTy+wnQhT5mKFERERERBRVmpuhxIBSKJktOUtOBk6c8J2dzeEA9u/XBpNaKjFRG4RyB42cTmDK\nFGDPHqBnT9Wol4Ehothls+kHjvRmiyQiIiIiorDX3IBSXDAOhixSWembhQS0zmxtRhlH+fkMIBFR\no0mT9DMtJ00K/rEQEREREVHQMKAUaazMQjLinuGIiKgp7hnQ5swB6urU+8fEiZwZjYiIiIgoyjGg\nRL4mTgz1ERBRJCkqYgCJiIiIiCjGcJa3WJaSomZhc2ck2e1qmTeGREREREREROQHM5RCybvxdTDZ\nbEBFhfqeASQiIiIiIiIiMoEZSqFUVaWCSp4SE4GcHOv2kZOjZoHz5HCoXidERERERERERAFghlKo\nGc2m5j2bW1wcMH++mmFNCN/1pfR9Tk4OsGWLpYdLRERERERERMSAUrjyFwiS0vxziIiIiIiIiIgs\nwpI3IiIiIiIiIiIyhQElIiIiIiIiIiIyhQElIiIiIiIiIiIyhQElIiIiIiIiIiIyhQElIiIiIiIi\nIiIyhQElIiIiIiIiIiIyhQElIiIiIiIiIiIyhQElIiIiIiIiIiIyhQElIiIiIiIiIiIyhQElIiIi\nIiIiIiIyhQElIiIiIiIiIiIyhQElIiIiIiIiIiIyhQElIiIiIiIiIiIyhQElIiIiIiIiIiIyhQEl\nIiIiIiIiIiIyhQElIiIiIiIiIiIyhQElIiIiIiIiIiIyhQElIiIiIiIiIiIyhQElIiIiIiIiIiIy\nhQElIiIiIiIiIiIyRUgpQ30MARFCHARQFurjsEhnAIdCfRBEEYzXEFHTeJ0QtQyvIaKm8Toharlw\nuI4ypZRdmlopYgNK0UQIsVFKeWGoj4MoUvEaImoarxOiluE1RNQ0XidELRdJ1xFL3oiIiIiIiIiI\nyBQGlIiIiIiIiIiIyBQGlMLDnFAfAFGE4zVE1DReJ0Qtw2uIqGm8TohaLmKuI/ZQIiIiIiIiIiIi\nU5ihREREREREREREpjCgFAAhRA8hxCohxFYhxBYhxP2u8U5CiA+FENtd/6a6xs8UQqwTQpwSQvze\nYztnCCG+9vg6LoR4wGCfI4QQ24QQO4QQkz3GHUKIL13P/0QI0ae1z5+opcLsGhrmuoY2CyEWCCHi\nWvv8iZojRNfJG0KIA0KIzV7juvskCmdhdg390nUM9UKIiJi5h2JDmF0nM4UQ3wkhNgkh/iGESGnN\ncyeyilXXkeux37m2sVkIsVgI0dZgn+Nc290uhBjnMT5NCPGDEOJEa55zw/5Y8maeEKI7gO5Syi+F\nEO0BfAHgBgB3ADgipZzuumFNlVL+QQjRFUCma50KKeWfdLZpB7APwCApZZnOY98DuArAXgCfA7hV\nSlkihPgewPVSyq1CiEIAA6WUd7TOmRNZI1yuIQDfASgD4JBSfi+EeBpAmZRybqucOJEJwb5OXI8P\nBXACwEIpZX+P8Rl6+7T6nImsFGbX0FkA6gHMBvB7KeVGi0+XKCBhdp0MB/CRlLJWCPECAPD/GooE\nVl1HQogMAJ8AyJFSnhRCvAVgqZRyvtf+OgHYCOBCANK1vwuklBVCiIuh7m+2Synbtfa5M0MpAFLK\n/VLKL13f/wRgK4AMANcDWOBabQHUCwRSygNSys8B1PjZrAPATr03XQADAeyQUu6SUlYDeNO1L0C9\ngDq4vu8IoDzgEyMKkjC6htIAnJJSfu9a70MAN7Xo5IgsEoLrBFLKNQCO6Dyku0+icBZO15CUcquU\nclug50LUWsLsOvlASlnrWlwP4HTzZ0QUfBZfR3EAEoWqmkiC/v391QA+lFIekVJWQN3DjHBte72U\ncr9lJ9cEBpRaSAiRBeA8ABsAdHP/8lz/djWxqVsALDZ4LAPADx7Le11jAHAXgKVCiL0AxgKYbmKf\nRCEX4mvoEIB4j/KDmwH0MLFPoqAI0nXiT0v2SRRyYXANEYW9MLtOJgB4v4XbIAq6llxHUsp9AP4E\nYA+A/QCOSSk/0FnVX3wgqBhQagEhRDsAbwN4QEp5vAXbSQBwHYC/G62iM+auVfwdgJFSytMBzAPw\nYqDHQRRsob6GpKr5vQXAS0KIzwD8BKBWZ12ikAnidUIUlXgNETUtnK4TIcQUqM9jzkC3QRQKLb2O\nXD2WrgeQDSAdQLIQYozeqjpjIellxIBSgIQQ8VAvFqeU8h3X8H9d9ZPuOsoDzdzcNQC+lFL+1/Xc\nHh4N7SZBRRw9syZOB1AuhOgC4Bwp5QbX+P8CuKRFJ0YUJOFwDQGAlHKdlPIyKeVAAGsAbG/puRFZ\nJcjXiT+B7pMopMLoGiIKW+F0nbiaC48GkO/6wx9RRLDoOsoDsFtKeVBKWQPgHQCXCCEGeVxH18HP\nvU2wcTajAAghBIC5ALZKKT0zgv4FYBxU2dk4AO82c5O3wiMtVEr5A4BzPfYXB6CvECIbqsHdLQBu\nA1ABoKMQop+rB8xVUPWaRGEtjK4hCCG6SikPCCHaAPgDgGmBnheRlYJ9nTQh0H0ShUyYXUNEYSmc\nrhMhxAioz2KXSymrmrk/opCz8DraA+BiIUQSgJNQ/cg2uhJIPO9tOgF4TjTOujscwKNWnItZnOUt\nAEKISwGsBfAt1IwdAPAYVJ3kWwB6Qr0YfimlPCKEOA2qC3sH1/onoDq3H3e9WH4A0EtKeczPPkcC\n+B8AdgBvSCmnucZvBPC0a7sVACZIKXdZfMpElgqza2gm1F/CbABmSSn/x+rzJQpEiK6TxQCuANAZ\nwH8BPCWlnCuESNPbp8WnTGSpMLuGbgTwFwBdABwF8LWU8mqLT5nItDC7TnYAaAPgsGvV9VJKZv9R\n2LP4OvojgF9DlX1+BeAuKeUpnX1OcO0DAKZJKee5xmdA/eE8HSpr6XUp5VTLT9p9HAwoERERERER\nERGRGeyhREREREREREREpjCgREREREREREREpjCgREREREREREREpjCgREREREREREREpjCgRERE\nREREREREpjCgREREREREREREpjCgREREREREREREpjCgREREREREREREpvx/hkJET82DSKcAAAAA\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f41ec520a10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "plt.plot(ground_true_df.times,ground_true_df.value, label = 'Actual')\n",
    "plt.plot(prediction_df.times,prediction_df.value,'ro', label='Predicted')\n",
    "plt.legend(loc='upper left')\n",
    "plt.savefig('result/bitcoin2015to2017_close_CNN_2_relu_result.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12356304.467087502"
      ]
     },
     "execution_count": 326,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "mean_squared_error(validation_original_outputs[:,:,0].reshape(-1),predicted_inverted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
